Efficient Fine-Tuning of DINOv3 Pretrained on Natural Images for Atypical Mitotic Figure Classification in MIDOG 2025
Detecting and classifying atypical mitotic figures (AMFs) in histopathological images remains challenging due to their low prevalence, subtle morphological features, and inter-observer variability in annotation. Method: We propose a lightweight, efficient transfer learning framework based on DINOv3-H+ Vision Transformer. It leverages natural-image pretraining from DINOv3 for cross-domain knowledge transfer, employs Low-Rank Adaptation (LoRA) for parameter-efficient fine-tuning, and integrates strong data augmentation to mitigate small-sample bias. Contribution/Results: Evaluated on the MIDOG 2025 preliminary screening test set, our method achieves a balanced accuracy of 0.8871—significantly outperforming baseline models. This work represents the first empirical validation of DINOv3’s strong generalization capability for digital pathology AMF recognition. Moreover, it establishes a reproducible, lightweight adaptation paradigm for pretrained foundation models in computational pathology.